Sökning: "gaussian mixture model"

Visar resultat 6 - 10 av 79 uppsatser innehållade orden gaussian mixture model.

1. 6. Exploring Normalizing Flow Modifications for Improved Model Expressivity

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Marcel Juschak; [2023]
   Nyckelord :Normalizing Flows; Motion Synthesis; Invertible Neural Networks; Glow; MoGlow; Maximum Likelihood Estimation; Generative models; normaliserande flöden; rörelsesyntes; inverterbara neurala nätverk; Glow; MoGlow; maximum likelihood-skattning generativa modeller;

   Sammanfattning : Normalizing flows represent a class of generative models that exhibit a number of attractive properties, but do not always achieve state-of-the-art performance when it comes to perceived naturalness of generated samples. To improve the quality of generated samples, this thesis examines methods to enhance the expressivity of discrete-time normalizing flow models and thus their ability to capture different aspects of the data. LÄS MER

3. 7. A study about Active Semi-Supervised Learning for Generative Models

   Master-uppsats, Linköpings universitet/Institutionen för datavetenskap

   Författare :Elisio Fernandes de Almeida Quintino; [2023]
   Nyckelord :Semi-Supervised Learning; Active Learning; Generative Models; Mixture Models; Semi-Övervakad Inlärning; Aktiv Inlärning; Generativa Modeller; Mixturmodeller;

   Sammanfattning : In many relevant scenarios, there is an imbalance between abundant unlabeled data and scarce labeled data to train predictive models. Semi-Supervised Learning and Active Learning are two distinct approaches to deal with this issue. LÄS MER

4. 8. Identification of driver baselines

   Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

   Författare :Alexander Malmgren; Fabian Daneshmand-Mehr; [2022-06-27]
   Nyckelord :computer; science; computer science; Time series clustering; clustering; ADAS; driver profile; statistics;

   Sammanfattning : This thesis aims to answer whether it is possible to produce one or more baselines based on naturalistic driving data collected over a period of 8 months. The baseline is based on variables extracted from the drivers action, such as acceleration and gaze vectors, along with variables extracted from the nature of the trip, such as time of day or road type. LÄS MER

5. 9. Out-of-distribution Recognition and Classification of Time-Series Pulsed Radar Signals

   Master-uppsats, KTH/Matematisk statistik

   Författare :Paul Hedvall; [2022]
   Nyckelord :Out-of-Distribution; Gaussian Mixture Models; Dirichlet Process Mixture Models; Deinterleaving; Radar classification; Time-series analysis; Pulsed radar signals; Out-of-Distribution; Gaussian Mixture Models; Dirichlet Process Mixture Models; Deinterleaving; Radar classification; Time-series analysis; Pulsed radar signals;

   Sammanfattning : This thesis investigates out-of-distribution recognition for time-series data of pulsedradar signals. The classifier is a naive Bayesian classifier based on Gaussian mixturemodels and Dirichlet process mixture models. LÄS MER

7. 10. Scenario Generation For Vehicles Using Deep Learning

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Jay Patel; [2022]
   Nyckelord :Scenario generation; Mixture Density Network; Gaussian Mixture Model; Autonomous driving; Semantic Graph Network; Scenariogenerering; Mixture Density Network; Gaussian Mixture Model; Autonom körning; Semantic Graph Network;

   Sammanfattning : In autonomous driving, scenario generation can play a critical role when it comes to the verification of the autonomous driving software. Since uncertainty is a major component in driving, there cannot be just one right answer to a prediction for the trajectory or the behaviour, and it becomes important to account for and model that uncertainty. LÄS MER